Computer-based health support systems (CBHSSs) are systems providing information, emotional support, decision support, and health status tracking for patients facing serious illness and injury. Such systems have been shown in several field tests and randomized clinical trials to improve quality of life of patients using them, and reduce costs of care for populations regardless of race, education or age. However, widespread successful implementation of such systems has had mixed success at best. One reason is that the implementation and diffusion of these technologies is a difficult and not well understood process. The primary purposes of this research are to (1) identify and understand the factors that influence implementation and adoption of CBHSS technologies and (2) quantify these factors and their relationships to develop a model to predict and explain the extent to which CBHSSs will be successfully implemented and diffused within a particular healthcare organization. There are four specific research aims in this project. First, we will identify and understand individual, organizational, technological, and environmental factors influencing the implementation and dissemination of CBHSSs in heath care provider organizations. Second, we will develop a quantitative model of these factors to predict and explain the implementation, dissemination, and institutionalization of CBHSSs within health care provider organization. Third, we will test the predictive validity of the model using both retrospective and prospective studies of carefully selected CBHSS past, current and future implementations. Fourth, we will develop and disseminate research products to assist organizations in the implementation, dissemination, and cost-effective use of CBHSSs. The study will involve a combination of research methods: a multiple case study design will be used to collect data on implementation factors; an expert-panel process will be used to build the quantitative model; a multiple case study design will be used to evaluate the initial model; a validation expert panel will refine and finalize the model; and prospective data collection will be performed to test the predictive value of the model.